Identifying,classifying and prioritizing factors affecting human errors in the mine design process: A mixed methods research

Abstract

BACKGROUND:

Mines are often home to many dangers with a high rate of accidents and occupational diseases. One of the most effective ways to prevent these adverse incidents is to identify and control the influential factors causing human error in design and the ensuing negative consequences.

OBJECTIVE:

This study aimed to explore, categorize and prioritize factors affecting human errors in the mine design process.

METHODS:

The study has a mixed-method design combining qualitative and quantitative data. In the qualitative phase, the required data were collected by conducting semi-structured interviews with 12 surface mine designers. The causes of errors were extracted and categorized by the latent content analysis using MAXQDA2022 software. The identified causes in the qualitative phase were sent to expert designers in Q tables, and the data were analyzed by factor analysis.

RESULTS:

Of the identified codes in the qualitative phase, 40 main themes in five different categories (individual, organizational, external, task, and environmental factors) were determined as causes. The results of the quantitative phase suggest the existence of four different mental patterns regarding the causes of design errors (DEs). The data analysis also shows that organizational and personal factors, particularly supervision and inspection, experience, and technical knowledge, were the strongest causes of DEs and environmental (hotness, coldness, indoor air quality, and noise) and external (work-family conflict) factors being the weakest ones.

CONCLUSION:

This study not only identifies and categorizes the causes of design errors in the mining industry but also suggests some control strategies for these errors based on the mental patterns of the experts.

Keywords

Accidents design error qualitative study Q-methodology mines

1 Introduction

Mines are one of the main factors in the development of countries as they play a crucial role in their economy [1 –3]. Nevertheless, numerous fatal accidents happen every year in this industry [4, 5]. Research shows that in addition to disastrous consequences, the rate of the occurrence of accidents is also high so that for an ordinary miner the rate is up to 10 times more than the average workers in the United States, Australia, and New Zealand [6]. Human errors are one of the main causes of accidents in the mining industry [7, 8]. It is usually the case that workers are to blame for the accidents, but they may play an insignificant role in the occurrence of accidents as the main cause is usually design errors [9]. However, not much attention is paid to this issue, and workers are regarded as the main causes of accidents. For example, end user error is constantly being reported as the main cause of accidents, paying no assiduous attention to the role of errors in designing equipment and devices as well as the situation in which the error happens [10]. Studies on the accidents demonstrate that in 20–50% of cases, at least one DEs has been identified as the main cause of the accident [11]. DEs are the most important cause of failure in buildings, bridges, and other engineering structures [12]. One of the main causes of death in and around mines is fly rock, the main reason being DEs [13]. Margolin reported that despite the many benefits of designing in human life, DEs is the main cause of the death of millions of people and the entry of millions of tons of air pollution into the atmosphere [14]. Many studies have highlighted the fundamental role of DE in safety and reliability engineering [15]. Such errors not only result in accidents and loss of life but also negatively influence the costs and schedules of projects [16]. DEs is one of the main reasons for rework [17]. Chao defines DEs as the result of a designer’s actions and decisions in product development leading to failure in planned or intended outcome [18]. Reason considers DEs a latent human error and believes it is part of the designing process that is not in line with the specified standards [19]. DEs can occur due to errors of commission or omission; in both cases, the error is unwanted and spontaneous [20]. A combination of factors can affect DEs, such as individual, organizational, and task factors [12]. The results of the study by Chao and Ishii showed that poor management, lack of standards, poor execution, insufficient knowledge in the field of design, inadequate communication, lack of awareness of external changes, and incorrect analysis are the root causes of DEs [21]. Some studies have confirmed the impacts of some variables on DEs, such as inadequate training, unrealistic design, and documentation schedules [22], lack of appropriate resources, financial pressure, personal ethics, government regulations, and designer training [23]. To control the economic and social consequences of accidents, it is critical to identify the factors affecting human error [24], but due to time and financial constraints, organizations try to identify and prioritize factors to select the best control strategies to prevent accidents [25]; therefore, it is more logical to refer to the expert opinions and rely on their professional expertise to single out the most significant factors [24]. Iran is where a large part of its economic and social development depends on mining [26]. It has 7% of the mineral recourses in the world [27]. Mining industries in Iran have experienced increased accidents [28] and important environmental and public health challenges [29]. Due to the significance of the problem and lack of a study that extensively surveys and prioritizes factors that can cause DEs, this study aimed at exploring, categorizing, and priorities factors affecting human error in surface mine design in Iran by using a mixed-method design based on expert opinions. The rest of this paper is arranged as follows: Section 2 explains the methodology and data collection procedures, Section 3 presents the results and the discussion, and Section 4 concludes the research, highlighting limitations and directions for future research.

2 Materials and methods

The current study is a combination of qualitative and quantitative methods. Mixed-method studies can present a more comprehensive picture of the phenomena by balancing the limitations of qualitative and quantitative data collection methods [30].

2.1 Identifying and classifying the effective factors of DE in mine design process

To identify and classifying the causes of human error in design, qualitative study was carried out according to the steps below.

2.1.1 Data collection

Semi-structured interviews were used to collect the data from 25 November to 27 December 2021. The duration of the interviews ranged from 45 minutes to 1 hour. The participants were selected out of the professional Iranian surface mine designers based on the following criteria: 1) knowledge, 2) ample experience, 3) easy accessibility, 4) willingness to participate in the study, and 5) availability to participate [31, 32]. In the first step, a database of experts was collected. Due to the diversity of minerals, the difference in the size of the mines, the geography of the design environment, and the variety of techniques and tools used in the design, attempts were made to select decision-makers whose experience covered the listed items. Finally, out of 150 Iranian Open Mines Designers Association members, 12 were purposefully selected. The entire sample population was calculated using the theoretical saturation criteria [33 –36]. In other words, data collection was halted when the researchers determined that more interviews and observations would not provide them with new information [37, 38]. Saturation has also been identified as the most commonly evoked justification and gold standard for sample size in qualitative research [38, 39]. In this study, after holding 12 interviews, the researcher reached theoretical saturation. The aims of the study were explained to the participants at the start of the interview sessions, and they were guaranteed confidentiality of their verbal responses, participant autonomy, the preservation of their anonymity, and interview duration. They were also allowed to refuse to answer questions or suspend the interview. When the interviewees agreed, they signed the consent form. The interview questions were developed through a review of the literature on DEs, with an emphasis on understanding the factors affecting this phenomenon as well as consultation with the research team.

2.1.2 Data analysis

The extraction process was based on the systematic Strauss-Corbin method, including open, axial, and selective coding [40]. After each interview, the researcher analyzed the content by MAXQDA 2022 software, followed by the next interview based on the feedback from the previous one.

2.1.3 Data trustworthiness

Lincoln and Guba’s trustworthiness criteria were employed to ensure the trustworthiness and credibility of the results [41]. To establish the reliability and validity of the results, the following steps were made: reviewing the results by a focus group made of the participating experts, reviewing the results by another group of experts not participating in the study (one faculty member and two design experts who were familiar with content analysis), long-term engagement of the researcher with the research environment, reviewing research questions several times to make sure of their intelligibility and unambiguousness, sending the research proposal to the participants to make them ready mentally, and reviewing the extracted codes several times by the researcher.

2.2 Prioritizing identified factors using Q-methodology

Q-methodology is a systematic study with statistical, epistemological, and psychological principles [42] that focuses on individuals’ subjective viewpoints [43]. This technique has been widely used in connection with important human issues such as the environment [44], health [45, 46], and safety [47]. Q-methodology is a research approach combining the quantitative rigor of statistical analysis with qualitative data [48] on the perception of DE management by people with design experience in surface mines.

2.2.1 Q set development and data gathering

After the identification of the causes of human error in design, the primary content of the discussion atmosphere formed. At this step, the content of the discussion atmosphere was analyzed, and a sample of expressions labeled as Q sample was selected. McKeown and Thomas suggested 30 to 100 Q samples [49], but Donner believed that a range of 20 to 60 Q samples is sufficient for the findings to be reliable and valid [50]. At the data collection step for designing cards, a Q plot was designed for 40 factors so that it could rank factors in a normally distributed fashion ranging from ‘completely agree’ (+6) to ‘completely disagree’ (-6). To facilitate the process of responding, a flash Q plot was used. The Q sample was selected based on the nonprobability sampling method [51] from those expert designers who provided the researcher with high-quality information and relevant viewpoints on the issue in question. Generally, 10–40 participants are asked to arrange the set of statements as the factors are comparable given the number of participants [48, 52]. Some studies have used Kaiser-Meyer-Olkin (KMO) test to determine the adequacy of the sample size. The results of these studies show that KMO values of more than 0.5 show that sampling is sufficient and acceptable for factor analysis [53 –57]. Shrestha stated that a KMO value > 0.6 is acceptable for sample sizes < 100, and a KMO value between 0.5 and 0.6 is acceptable for sample sizes between 100 and 200 [58].

2.2.2 Factor analysis

The IBM SPSS V26 Software package was used to enter information in this study. The conducted factor analysis procedure included factor extraction, factor rotation, and deciding on the number of factors to retain for interpretation. In the Q identification method, the expert participants who are similar in expressing their agreement or disagreement on ranking extracted themes share similar mentalities. Therefore, in this method, the categorizations are done based on the similarity of the participants’ mentalities [59]. In the Q method, the comprehensiveness of factors is important for the validity of findings, representing various mentalities [60]. To improve the validity of the findings in this study, the Q sample was first reviewed by investigating the relevant literature and then by interviewing some of the participants. After receiving feedback from the interviews and resolving ambiguities, the Q cards of the study were finalized. To check for the reliability of the questionnaire, it was sent to 20% of the participants again, yielding a correlation coefficient of above 80% [61].

3 Results and discussion

The current mixed-method study was conducted on the expert opinions of Iranian surface mine designers using qualitative and quantitative data. Table 1 demonstrates the demographic information of the participants. After the primary coding of interviews and specifying significant meaning units, the primary codes were extracted. The categorization of primary codes was carried out either by creating new codes or using central, abstract codes.

Table 1
Demographic variables of the experts

Qualitative study Q study

Demographic Total Percentage Total Percentage

variables

Gender

Male 9 80.00% 10 83.3%

Female 3 20.00% 2 16.7%

Educational

Bachler 1 6.67% 4 33.3%

Master 6 40.00% 7 58.3%

Doctoral 5 53.33% 1 8.4%

Experience in mine design

<5 years – – 3 25%

5–15 years 7 58.33% 8 66.6%

>15 years 5 41.67% 1 8.4%

	Qualitative study	Q study
Gender
Male	9	80.00%	10	83.3%
Female	3	20.00%	2	16.7%
Educational
Bachler	1	6.67%	4	33.3%
Master	6	40.00%	7	58.3%
Doctoral	5	53.33%	1	8.4%
Experience in mine design
<5 years	–	–	3	25%
5–15 years	7	58.33%	8	66.6%
>15 years	5	41.67%	1	8.4%

Next, the second round of coding was done by creating axial codes. The researcher consulted with his research supervisor and advisor to assign the main categories. Eventually, 40 main themes in five categories (personal, organizational, environmental, task, and external factors) were explored. Figure 2 shows the identified subcategories and main categories by the experts. The identified categories were then given to the experts with different opinions in the form of cards to express their opinions. Figure 3 depicts a completed sample of the Q table. The collected data were then entered into SPSS. Before running factor analysis, KMO and Bartlett’s Sphericity tests were done to test the adequacy of sample size and sphericity of the data; the KMO value (0.672) was found to be higher than 0.5, hence proving that the sample size was adequate for running factor analysis [62 –64]. The results of Bartlett’s Sphericity coefficient < 0.01 (this coefficient was 0.00 in this study) and the significance of Bartlett’s Sphericity test at p < 0.01 supported our datasets to be fitted for the factor analysis [65]. The factor analysis results showed that four factors had Eigenvalues above 1 (Fig. 4). Based on Kaiser’s Principle, factors are retained if the Eigenvalue is higher than 1 in the Scree plot [66]. Thus, it can be stated that the participating experts in the study can be categorized into four groups based on their mentalities. Yet, before having rotations, no simple and structured model is reached [67]. Given the number of experts in this study, the varimax rotation was used to reach the final model, and the resulting matrix of questions and factors after varimax rotation is shown in Table 2. The first to fourth opinions percentages were 23.496%, 16.668%, 15.46%, and 13.729%, respectively, equaling 69.353% of the total variance. Table 3 demonstrates the correlation coefficients between Q plots and the factors. As experts were categorized into four groups with correlated answers revolving around the same themes and the most important agreed and disagreed points (Fig. 5), it can be concluded that the experts evaluated DEs in the mining process based on four different mentalities as follows:

Fig. 1

Roadmap and steps of conducting the study [75, 76].

Fig. 2

The explored causes of design errors.

Fig. 3

Example of completed Q table.

Fig. 4

Scree plot for identifying factors.

Table 2

The matrix of experts and factors after varimax rotation

	Component
	1	2	3	4
Expert 1	0.721	0.013	0.046	–0.272
Expert 2	0.666	0.468	–0.041	0.103
Expert 3	0.371	–0.356	0.481	0.481
Expert 4	0.346	–0.575	0.124	0.597
Expert 5	0.195	0.556	0.572	–0.116
Expert 6	0.555	0.357	0.486	0.128
Expert 7	0.646	–0.128	–0.168	–0.069
Expert 8	0.615	–0.437	–0.380	–0.014
Expert 9	0.372	0.249	–0.600	0.188
Expert 10	0.784	–0.016	–0.014	–0.362
Expert 11	0.564	–0.539	0.210	–0.411
Expert 12	0.520	0.538	–0.289	0.372

Table 3

Eigenvalue and dispersion of factors

Component	Initial eigenvalues			Extraction sums of			Rotation sums of
				squared loadings			squared loadings
	Total	% of	Cumulative	Total	% of	Cumulative	Total	% of	Cumulative
		variance	%		variance	%		variance	%
1	3.704	30.869	30.869	3.704	30.869	30.869	2.820	23.496	23.496
2	1.963	16.356	47.225	1.963	16.356	47.225	2.000	16.668	40.164
3	1.474	12.286	59.511	1.474	12.286	59.511	1.855	15.460	55.624
4	1.181	9.843	69.353	1.181	9.843	69.353	1.647	13.729	69.353
5	0.784	6.531	75.884
6	0.721	6.008	81.892
7	0.615	5.125	87.017
8	0.444	3.698	90.715
9	0.405	3.371	94.087
10	0.306	2.554	96.640
11	0.248	2.069	98.709
12	0.155	1.291	100.000

Fig. 5

An overview of designers’ perspectives on the factors influencing DEs.

3.1 Mentality (perspective) no. 1

Mentality No. 1 represents 23.49% of participants’ opinions regarding the significance of each factor in influencing DEs with an Eigenvalue of 2.82. The similar opinions of the participants of this group show that they consider supervision and inspection, experience, and designer’s skill as the main causes of DEs. They also believed that the least important factors were personality type, designer’s age, thermal comfort, and poor air quality.

3.2 Mentality (perspective) no. 2

This factor represents 16.66% of participants’ opinions with an Eigenvalue of 2.00. According to the participants’ opinions of this group, design experience, supervision and inspection, and technical knowledge are the strongest factors causing DEs, and family-work conflict and circadian rhythm are the least effective factors.

3.3 Mentality (perspective) no. 3

This factor represents 15.46% of participants’ opinions with an Eigenvalue of 1.85. The shared opinions of the participants of this group showed that supervision and inspection, design process type, technical knowledge, and the complexity of mine design are the most significant causes of DEs, and the designer’s risk perception, the designer’s nutrition, and family-work conflict are the least significant causes.

3.4 Mentality (perspective) no. 4

This type of mentality represents 14% of the participants’ opinions with an Eigenvalue of 1.64. The shared opinions of the participants of this group show that supervision and inspection, pressure from legal organizations, and job interest are the most effective factors causing DEs. On the other hand, poor lighting and noise in the workplace and inadequate training are considered the least effective factors. The mentalities of the expert participants were divided into four groups. The study results show that the first and second groups comprise 40.15% of all participants. It can be argued that experts in all four groups believed that supervision and inspection are the key factors in DE, but they also differ in terms of some other factors. The participants in the first group believed that supervision and inspection and poor management knowledge are the two significant organizational factors that, together with some personal factors such as inadequate experience and skills, can influence the occurrence of human error in design. They mentioned that improving control level and management knowledge in an organization can result in employing more experienced and skillful designers leading to more effective designs. Supervision and inspection have always been considered the main causes of accidents. For example, this factor has been reported to be the main reason behind 37 accident cases out of the 84 overall cases in the railing industry [68]. Scott states that the analysis of the role of human error in accidents shows that personal variables and control errors are the two main root causes of human errors [69]. The expert participants in the second group reported that organizational factors (supervision and inspection and inappropriate leadership style) and the designer’s inadequate experience and technical knowledge could result in employing inexperienced designers in mining projects. Gue’s study revealed that the managers’ responsibility affects workers’ knowledge through social support and production pressure. Improving individuals’ knowledge can also significantly reduce human error [70]. The participants in the third group believed that a combination of organizational factors (e.g., supervision and inspection, designer’s inaccessibility to software and hardware resources) and task factors (e.g., the complexity of mine design and short-term/long-term design processes) together with personal factors (e.g., inadequate technical knowledge) could cause a human error in design. They stated that mine design is a complex job due to the existence of a lot of uncertainties, especially for long-term designs. The complexity of systems is one of the main reasons for human error [71]. Hence, the reduction of the complexity of systems can decrease the chance of the occurrence of errors [47]. The designer’s inaccessibility to appropriate hardware and software resources and inadequate technical knowledge can also increase the complexity of my design. In other words, if a designer has easy access to adequate hardware and software resources, designing becomes much easier for him/her. Finally, the participants of the fourth group accounted for 14% of the participants showing that their opinions were more specific. They believed that a mixture of external, organizational, and individual factors comprises the main causes of human error in design. The findings of the study demonstrate that supervision and inspection is the most significant factor among all types of mentalities expressed by the expert participants of the study. Previous studies show that 30% -40% of human errors belong to organizational issues. Love et al. maintain that design audits, reviews, and verifications are the key factors increasing the chance of human error [72]. Lopez et al. also believe that employing inexperienced designers with low technical knowledge and engaging underqualified designers in important design projects are the main causes of DE in organizations [12]. In accordance with the theories of the analysis of human error and unsafe behavior, the random combination of some factors can enhance the chance of occupational accidents [73]; thus, to prevent human error as a systematic process, the most significant variables playing a role in this regard need to be identified and categorized [24]. Mixed-method research can solve complex problems and provide a better understanding of the phenomena [74]. Consequently, this mixed-method study used qualitative and quantitative (Q method) data to identify the causes of human error in design and investigate the mental patterns to manage and control such causes.

4 Conclusion

DEs is preventable and controllable in many cases. It is possible to develop and implement appropriate control strategies by identifying, classifying, and prioritizing effective factors. The mixed-method can help identify and categorize the causes of DEs and the mental patterns of designers based on sociocultural factors. In the qualitative part of the study, 40 main themes in five different categories were explored. In the quantitative part of the study, four models for prioritizing and controlling DEs were generated from the perspective of experts. Based on these models, the effects of some themes such as supervision and inspection, the complexity of mine design, pressure from legal organizations, experience, technical knowledge, and skill occurrence of DEs are more significant than others. Therefore, control strategies in surface mine design should focus on managing these factors. One main advantage of this study was that we explored factors causing DEs from experts’ perspectives, thus improving the accuracy of findings. Despite the best efforts, the study had some limitations, which provided further research opportunities. The main limitation of the study was the sample size of the participants, which is an inherent limitation of qualitative and Q methodology studies. The results of this study can be tested in large-scale quantitative future studies. Further, the lack of statistical validity of the results from the qualitative study was another limitation of the study. In this study, the analysis of the cause-and-effect relationship among factors was not discussed. Future research may be carried out using other techniques such as interpretive structural modeling (ISM), Fuzzy DEMATEL, and structural equation modelling (SEM) to understand the hierarchical inter-relationships among identified factors. Besides, researchers may extend this work by using other relevant multi-criteria decision analysis methods, such as Fuzzy AHP and Fuzzy Delphi, to prioritize the effective factors. Moreover, this study is one of the first of its kind studying factors causing DEs in the surface mine design. Hence it isn’t easy to generalize the findings to underground mines or other industries.

Ethical approval

The study program was approved by the National Committee for Ethics in Medical Research (protocol code IR.UMSHA.REC.1400.617).

Informed consent

All participants provided informed consent.

Conflict of interest

The authors have no conflict of interest to declare.

Footnotes

Acknowledgments

The authors would like to thank the members of the expert panel and all participants for contributing their time and effort to the research.

Funding

This research was funded by the Hamedan University of Medical Sciences and Health Services (grant number 140008257113).

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	Qualitative study		Q study
Demographic	Total	Percentage	Total	Percentage
variables
Gender
Male	9	80.00%	10	83.3%
Female	3	20.00%	2	16.7%
Educational
Bachler	1	6.67%	4	33.3%
Master	6	40.00%	7	58.3%
Doctoral	5	53.33%	1	8.4%
Experience in mine design
<5 years	–	–	3	25%
5–15 years	7	58.33%	8	66.6%
>15 years	5	41.67%	1	8.4%